针对难以及时地获取充足而准确的遥感样本、缺乏积累和管理遥感样本的有效手段是制约遥感图像分类技术发展的瓶颈问题。构建了基于改进型模糊ARTMAP网络的CBR（case-basedr easoning范例推理）遥感图像分类系统。系统将改进型模糊ARTMAP网络作为范例的知识提取器和图像分类器，运用CBR求解策略实现遥感样本知识的合理储备、优化组合和重复利用。分别应用本文所建系统、最大似然法、BP网络和改进型模糊ARTMAP网络对向海自然保护区TM遥感图像进行分类操作，实验结果表明，本文建立的系统与其他分类方法相比，能够更好地提高遥感样本数据的利用效率和遥感图像的分类精度，而且一定程度上解决了在样本有限的条件下如何高效利用已有数据进行遥感图像分类的问题。

Difficulties in obtaining satisfied sample data of remote sensing image and the lack of effective methods to accumulate and manage them are the bottlenecks for the development of classification technology. Against these problems, a CBR(case based reasoning) system for remote sensing classification is established on the base of modified Fuzzy ARTMAP neural network in this paper. In the CBR system, the modified Fuzzy ARTMAP neural network plays the roles of the knowledge extractor of cases and the classifier of remote sensing images. Reasonable reserve, optimization combination and reutilization of remote sensing samples can be implemented with the CBR strategy. The TM image of Xiang Hai nature reserve is classified by following four methods respectively: the CBR system, maximum likelihood, BPNN and modified Fuzzy ARTMAP. Experimental results show that, comparing with other classification methods, both the utilization efficiency of remote sensing samples and the classification accuracy of images can be greatly improved by the CBR system. The problem of how to utilize the existing samples efficiently for remote sensing classification when the sample data of the current image are limited can be solved to a certain extent in the proposed system.